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Publications [#339305] of David B. Dunson


Papers Published

  1. Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian Conditional Density Filtering, Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 27 no. 3 (July, 2018), pp. 657-672, Informa UK Limited [doi]
    (last updated on 2019/06/17)

    © 2018, © 2018 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. We propose a conditional density filtering (C-DF) algorithm for efficient online Bayesian inference. C-DF adapts MCMC sampling to the online setting, sampling from approximations to conditional posterior distributions obtained by propagating surrogate conditional sufficient statistics (a function of data and parameter estimates) as new data arrive. These quantities eliminate the need to store or process the entire dataset simultaneously and offer a number of desirable features. Often, these include a reduction in memory requirements and runtime and improved mixing, along with state-of-the-art parameter inference and prediction. These improvements are demonstrated through several illustrative examples including an application to high dimensional compressed regression. In the cases where dimension of the model parameter does not grow with time, we also establish sufficient conditions under which C-DF samples converge to the target posterior distribution asymptotically as sampling proceeds and more data arrive. Supplementary materials of C-DF are available online.
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